Chinese Journal of Mechanical Engineering >

2023 , Vol. 36 >Issue 1: 7 - 7

DOI: https://doi.org/10.1186/s10033-023-00846-0

2023-02-25

Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

  • Xinghao Du ,
  • Jinhao Meng ,
  • Kailong Liu ,
  • Yingmin Zhang ,
  • Shunli Wang ,
  • Jichang Peng ,
  • Tianqi Liu
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  • 1. College of Electrical Engineering, Sichuan University, Chengdu, 610044, China;
    2. School of Electrical Engineering, Xi'an Jiaotong University, 710049, Xi'an, China;
    3. Warwick Manufacturing Group, University of Warwick, Coventry, UK;
    4. Southwest University of Science and Technology, Mianyang, 621010, China;
    5. Nanjing Institute of Technology, Nanjing, 211103, China

Received date: 2021-09-23

  Revised date: 2022-10-13

  Online published: 2023-12-20

Supported by

Supported by National Natural Science Foundation of China (Grant No. 52107229), the Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province (Grant No. 20KFKT02)

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Abstract

Online parameter identification is essential for the accuracy of the battery equivalent circuit model (ECM). The traditional recursive least squares (RLS) method is easily biased with the noise disturbances from sensors, which degrades the modeling accuracy in practice. Meanwhile, the recursive total least squares (RTLS) method can deal with the noise interferences, but the parameter slowly converges to the reference with initial value uncertainty. To alleviate the above issues, this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM. RLS converges quickly by updating the parameters along the gradient of the cost function. RTLS is applied to attenuate the noise effect once the parameters have converged. Both simulation and experimental results prove that the proposed method has good accuracy, a fast convergence rate, and also robustness against noise corruption.

Key words： Li-ion battery; Equivalent circuit model; Recursive least squares; Recursive total least squares

Cite this article

Xinghao Du , Jinhao Meng , Kailong Liu , Yingmin Zhang , Shunli Wang , Jichang Peng , Tianqi Liu . Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise[J]. Chinese Journal of Mechanical Engineering, 2023 , 36(1) : 7 -7 . DOI: 10.1186/s10033-023-00846-0

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